{
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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n==============\nSGD: Penalties\n==============\n\nContours of where the penalty is equal to 1\nfor the three penalties L1, L2 and elastic-net.\n\nAll of the above are supported by\n:class:`sklearn.linear_model.stochastic_gradient`.\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nl1_color = \"navy\"\nl2_color = \"c\"\nelastic_net_color = \"darkorange\"\n\nline = np.linspace(-1.5, 1.5, 1001)\nxx, yy = np.meshgrid(line, line)\n\nl2 = xx ** 2 + yy ** 2\nl1 = np.abs(xx) + np.abs(yy)\nrho = 0.5\nelastic_net = rho * l1 + (1 - rho) * l2\n\nplt.figure(figsize=(10, 10), dpi=100)\nax = plt.gca()\n\nelastic_net_contour = plt.contour(xx, yy, elastic_net, levels=[1],\n                                  colors=elastic_net_color)\nl2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)\nl1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)\nax.set_aspect(\"equal\")\nax.spines['left'].set_position('center')\nax.spines['right'].set_color('none')\nax.spines['bottom'].set_position('center')\nax.spines['top'].set_color('none')\n\nplt.clabel(elastic_net_contour, inline=1, fontsize=18,\n           fmt={1.0: 'elastic-net'}, manual=[(-1, -1)])\nplt.clabel(l2_contour, inline=1, fontsize=18,\n           fmt={1.0: 'L2'}, manual=[(-1, -1)])\nplt.clabel(l1_contour, inline=1, fontsize=18,\n           fmt={1.0: 'L1'}, manual=[(-1, -1)])\n\nplt.tight_layout()\nplt.show()"
      ]
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